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DeepDisco: A Deep Learning Tool for Rapid Brain Connectivity Estimation

Matsulevits, A.; Thiebaut de Schotten, M.; Tourdias, T.

2025-07-15 neuroscience
10.1101/2025.07.10.663897 bioRxiv
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DeepDisco is a cross-platform software that uses deep learning to rapidly predict brain connectivity maps from binary regions or lesions. It offers a user-friendly and scriptable interface, making indirect connectivity analysis accessible for large datasets and scalable for AI frameworks.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

1
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Top 0.2%
10.8%
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2
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Top 0.1%
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3
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6
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7
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Top 2%
5.4%
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50% of probability mass above
8
Scientific Reports
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Top 18%
5.1%
9
Nature
○ 645 papers in training set
Top 3%
5.1%
10
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● 126 papers in training set
Top 0.4%
4.8%
11
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○ 252 papers in training set
Top 2%
3.4%
12
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Top 0.3%
3.2%
13
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Top 2%
2.3%
14
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● 20 papers in training set
Top 0.2%
2.1%
15
Science Advances
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Top 21%
1.7%
16
eLife
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Top 56%
1.3%
17
Frontiers in Neuroinformatics
○ 41 papers in training set
Top 0.4%
1.3%
18
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● 282 papers in training set
Top 4%
1.0%
19
PLOS Computational Biology
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Top 18%
1.0%
20
Cell Reports Methods
○ 165 papers in training set
Top 4%
0.8%
21
Patterns
○ 78 papers in training set
Top 3%
0.6%
22
Neuroinformatics
○ 46 papers in training set
Top 1.0%
0.6%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.